Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Huang, Wenli; Swatantran, A.; Johnson, Kristofer D.; Duncanson, Laura; Tang, Hao; Dunne, Jarlath O’Neil; Hurtt, G. C.; Dubayah, Ralph

doi:10.1186/s13021-015-0030-9

Cited by 32 publications

(36 citation statements)

References 22 publications

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“…Spatial and temporal match of field sampling and remote sensing data significantly influence model accuracy [80,81]. Intuitively, very accurate models need to be site-specific, species-specific and scale-dependent.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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Section: Discussionmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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“…Maryland (37.8°N-39.8°N, 75.0°W-79.5°W) lies in the mid-Atlantic region of the United States, bordering Virginia and Washington to the south and west and covering a land area of ~25,600 km 2 [39]. The east of Maryland is relatively flat with elevations from 0-200 m and has a humid subtropical climate, with hot, humid summers and short, cool winters, while the opposite region around the Appalachian Mountains is higher and more complicated with elevations from 150-900 m and has a humid continental climate.…”

Section: Study Areamentioning

confidence: 99%

Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

2018

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Abstract:Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R 2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.

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“…It is equipped with a dual wedge scanner allowing a full conical observing angle from 0 • to 40 • . HRQLS was flown over Garrett county of Maryland, USA in early September of 2013 to support a NASA Carbon Monitoring System (CMS) project over the state of Maryland [15,16]. The flight survey required less than 12 h to cover the entire county (~1700 km 2 ) with 50% overlap of flight lines.…”

Section: Hrqlsmentioning

confidence: 99%

“…Field measurements were collected during the summer of 2013 with a main survey focus on aboveground biomass estimates [15]. Field plots (fixed and variable radius) were selected using a stratified random sampling based on distributions of land cover type and canopy height.…”

Section: Reference Datamentioning

confidence: 99%

“…It means that these methods forfeit a key advantage of SPL data over other systems-the ability to provide fine scale topographic and structural details. Applying histogram based filtering methods to SPL data would therefore drastically reduce its usefulness for applications such as high-quality topographic mapping [12], individual tree-level classification [13] and high resolution carbon monitoring [14,15]. Furthermore, histogram based filtering methods could potentially have errors over complex terrain because of the mixing between ground/canopy signals and noise points.…”

Section: Introductionmentioning

confidence: 99%

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Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

et al. 2016

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Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r 2 = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidar heights (r 2 = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r 2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r 2 = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data.

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Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Cited by 32 publications

References 22 publications

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

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